The fundamental goal of this work is to improve our ability to optimize the overall plan for each patient's[unreadable] treatment course ("comprehensive" treatment course optimization). Current planning methods make use of[unreadable] only a static single instance of the patient's anatomy (typically a treatment planning CT scan), and create an[unreadable] optimized treatment plan based on the clinical therapy prescription, using interactive or inverse planning[unreadable] optimization techniques (for Intensity Modulated Radiation Therapy (IMRT)). However, our knowledge of[unreadable] the patient and treatment goals is not static, but dynamic. Throughout the patient's course of treatment, we[unreadable] obtain new information on geometrical localization, inter- and intra-fraction motion, clinical response, and[unreadable] the precision to which we can predict uncertainties in the data used for planning. To better account for and[unreadable] utiltize this knowledge, the proposed research will develop and evaluate new paradigms for comprehensive[unreadable] optimization of the entire treatment course, evaluating potential improvements over the single-instance[unreadable] planning/optimization techniques which are widely used throughout the radiotherapy community. The[unreadable] project will develop a planning/optimization framework for individualized optimization which incorporates[unreadable] geometric, dosimetric, clinical and biological information as it becomes available (Aim 1), study[unreadable] improvements in single-stage optimization procedures for the multi-criteria problems encountered in[unreadable] therapy planning (Aim 2), and explicitly investigate multi-stage optimization methods for planning and[unreadable] delivery of the entire treatment course (Aim 3). This project will show that development of plan optimization[unreadable] strategies which 1) explicitly account for setup uncertainty and/or organ motion, 2) make use of updated[unreadable] clinical, biological, dosimetric and geometric information to refine the plan, and 3) model and optimize[unreadable] multi-stage adaptive therapy for the entire treatment course will better tailor the overall treatment plan to[unreadable] each individual patient and predict improvements over the current static inverse plan generated once for[unreadable] each patient.